In this project you will apply statistics, AI, and/or machine learning methodologies for subsurface modelling to include heterogeneous data streams (borehole data, cone penetration tests, seismic data) sourced from new data collection projects. For instance, the prediction of geothermal potential, and associated impacts on drinking water supply and seismicity require high resolution subsurface models up to 1200 m containing information on a range of characteristics, such as lithology, facies, flow and thermal properties. Many subsurface-related projects currently encounter bottlenecks regarding the detail, coverage, and quality of subsurface data.

With new end user requirements and a more diverse, possibly interfering, use of the subsurface data, extending and improving subsurface information is required. Relying on large and diverse geological data sets and advanced geostatistical modelling methodologies, the resulting information contains uncertainties. To support planning of engineering and mining activities on and below the surface, the Geological Survey of the Netherlands (TNO) creates and maintains consistent, nation-wide digital 3D models of the subsurface. Are you interested in statistics, AI, or machine learning and are you eager to apply these methods to solve problems in geology? We welcome you to apply for a position at the Department of Physical Geography at Utrecht University (DPG-UU) in the Netherlands, for a joint project with TNO Geological Survey of the Netherlands.

Planet earth 3d model